{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "test_set = pd.read_csv('E:\\dataset\\TIANCHI_ad\\\\test.txt',sep=' ')\n",
    "train_set = pd.read_csv('E:\\dataset\\TIANCHI_ad\\\\train.txt',sep=' ')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 18371 entries, 0 to 18370\nData columns (total 26 columns):\ninstance_id                  18371 non-null int64\nitem_id                      18371 non-null int64\nitem_category_list           18371 non-null object\nitem_property_list           18371 non-null object\nitem_brand_id                18371 non-null int64\nitem_city_id                 18371 non-null int64\nitem_price_level             18371 non-null int64\nitem_sales_level             18371 non-null int64\nitem_collected_level         18371 non-null int64\nitem_pv_level                18371 non-null int64\nuser_id                      18371 non-null int64\nuser_gender_id               18371 non-null int64\nuser_age_level               18371 non-null int64\nuser_occupation_id           18371 non-null int64\nuser_star_level              18371 non-null int64\ncontext_id                   18371 non-null int64\ncontext_timestamp            18371 non-null int64\ncontext_page_id              18371 non-null int64\npredict_category_property    18371 non-null object\nshop_id                      18371 non-null int64\nshop_review_num_level        18371 non-null int64\nshop_review_positive_rate    18371 non-null float64\nshop_star_level              18371 non-null int64\nshop_score_service           18371 non-null float64\nshop_score_delivery          18371 non-null float64\nshop_score_description       18371 non-null float64\ndtypes: float64(4), int64(19), object(3)\nmemory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "test_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 478138 entries, 0 to 478137\nData columns (total 27 columns):\ninstance_id                  478138 non-null int64\nitem_id                      478138 non-null int64\nitem_category_list           478138 non-null object\nitem_property_list           478138 non-null object\nitem_brand_id                478138 non-null int64\nitem_city_id                 478138 non-null int64\nitem_price_level             478138 non-null int64\nitem_sales_level             478138 non-null int64\nitem_collected_level         478138 non-null int64\nitem_pv_level                478138 non-null int64\nuser_id                      478138 non-null int64\nuser_gender_id               478138 non-null int64\nuser_age_level               478138 non-null int64\nuser_occupation_id           478138 non-null int64\nuser_star_level              478138 non-null int64\ncontext_id                   478138 non-null int64\ncontext_timestamp            478138 non-null int64\ncontext_page_id              478138 non-null int64\npredict_category_property    478138 non-null object\nshop_id                      478138 non-null int64\nshop_review_num_level        478138 non-null int64\nshop_review_positive_rate    478138 non-null float64\nshop_star_level              478138 non-null int64\nshop_score_service           478138 non-null float64\nshop_score_delivery          478138 non-null float64\nshop_score_description       478138 non-null float64\nis_trade                     478138 non-null int64\ndtypes: float64(4), int64(20), object(3)\nmemory usage: 98.5+ MB\n"
     ]
    }
   ],
   "source": [
    "train_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_set['dayofweek'] = (train_set['context_timestamp']/(60*60*24)).apply(np.floor)%7\n",
    "train_set['hourofday'] = (train_set['context_timestamp']/(60*60)).apply(np.floor)%24\n",
    "train_set['minofday'] = (train_set['context_timestamp']/(60)).apply(np.floor)%(24*60)\n",
    "\n",
    "\n",
    "test_set['is_trade'] = -1\n",
    "test_set['dayofweek'] = (test_set['context_timestamp']/(60*60*24)).apply(np.floor)%7\n",
    "test_set['hourofday'] = (test_set['context_timestamp']/(60*60)).apply(np.floor)%24\n",
    "test_set['minofday'] = (test_set['context_timestamp']/(60)).apply(np.floor)%(24*60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17798.0"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_set['context_timestamp']/(60*60*24)).apply(np.floor).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(test_set['context_timestamp']/(60*60*24)).apply(np.floor).max()%7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_4_rate = train_set[train_set['dayofweek'] != 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400644, 30)"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set_4_rate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add item_id_rate\n",
    "item_id_rate = train_set_4_rate.groupby(['item_id'])['is_trade'].mean().reset_index()\n",
    "\n",
    "item_id_rate.columns = ['item_id','item_id_rate']\n",
    "\n",
    "train_add_item_id_rate = pd.merge(train_set,item_id_rate,how='left',on=['item_id'])\n",
    "test_add_item_id_rate = pd.merge(test_set,item_id_rate,how='left',on=['item_id'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add item_brand_id_rate\n",
    "item_brand_id_rate = train_set_4_rate.groupby(['item_brand_id'])['is_trade'].mean().reset_index()\n",
    "\n",
    "item_brand_id_rate.columns = ['item_brand_id','item_brand_id_rate']\n",
    "\n",
    "train_add_item_brand_id_rate = pd.merge(train_add_item_id_rate,item_brand_id_rate,how='left',on=['item_brand_id'])\n",
    "test_add_item_brand_id_rate = pd.merge(test_add_item_id_rate,item_brand_id_rate,how='left',on=['item_brand_id'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add item_city_id_rate\n",
    "item_city_id_rate = train_set_4_rate.groupby(['item_city_id'])['is_trade'].mean().reset_index()\n",
    "\n",
    "item_city_id_rate.columns = ['item_city_id','item_city_id_rate']\n",
    "\n",
    "train_add_item_city_id_rate = pd.merge(train_add_item_brand_id_rate,item_city_id_rate,how='left',on=['item_city_id'])\n",
    "test_add_item_city_id_rate = pd.merge(test_add_item_brand_id_rate,item_city_id_rate,how='left',on=['item_city_id'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add user_id rate\n",
    "user_id_rate = train_set_4_rate.groupby(['user_id'])['is_trade'].mean().reset_index()\n",
    "\n",
    "user_id_rate.columns = ['user_id','user_id_rate']\n",
    "\n",
    "train_add_user_id_rate = pd.merge(train_add_item_city_id_rate,user_id_rate,how='left',on=['user_id'])\n",
    "test_add_user_id_rate = pd.merge(test_add_item_city_id_rate,user_id_rate,how='left',on=['user_id'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add shop_id rate\n",
    "shop_id_rate = train_set_4_rate.groupby(['shop_id'])['is_trade'].mean().reset_index()\n",
    "\n",
    "shop_id_rate.columns = ['shop_id','shop_id_rate']\n",
    "\n",
    "train_add_shop_id_rate = pd.merge(train_add_user_id_rate,shop_id_rate,how='left',on=['shop_id'])\n",
    "test_add_shop_id_rate = pd.merge(test_add_user_id_rate,shop_id_rate,how='left',on=['shop_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add minofday rate\n",
    "minofday_rate = train_set_4_rate.groupby(['minofday'])['is_trade'].mean().reset_index()\n",
    "\n",
    "minofday_rate.columns = ['minofday','minofday_rate']\n",
    "\n",
    "train_all = pd.merge(train_add_shop_id_rate,minofday_rate,how='left',on=['minofday'])\n",
    "test_all = pd.merge(test_add_shop_id_rate,minofday_rate,how='left',on=['minofday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 18371 entries, 0 to 18370\nData columns (total 36 columns):\ninstance_id                  18371 non-null int64\nitem_id                      18371 non-null int64\nitem_category_list           18371 non-null object\nitem_property_list           18371 non-null object\nitem_brand_id                18371 non-null int64\nitem_city_id                 18371 non-null int64\nitem_price_level             18371 non-null int64\nitem_sales_level             18371 non-null int64\nitem_collected_level         18371 non-null int64\nitem_pv_level                18371 non-null int64\nuser_id                      18371 non-null int64\nuser_gender_id               18371 non-null int64\nuser_age_level               18371 non-null int64\nuser_occupation_id           18371 non-null int64\nuser_star_level              18371 non-null int64\ncontext_id                   18371 non-null int64\ncontext_timestamp            18371 non-null int64\ncontext_page_id              18371 non-null int64\npredict_category_property    18371 non-null object\nshop_id                      18371 non-null int64\nshop_review_num_level        18371 non-null int64\nshop_review_positive_rate    18371 non-null float64\nshop_star_level              18371 non-null int64\nshop_score_service           18371 non-null float64\nshop_score_delivery          18371 non-null float64\nshop_score_description       18371 non-null float64\nis_trade                     18371 non-null int64\ndayofweek                    18371 non-null float64\nhourofday                    18371 non-null float64\nminofday                     18371 non-null float64\nitem_id_rate                 18142 non-null float64\nitem_brand_id_rate           18345 non-null float64\nitem_city_id_rate            18369 non-null float64\nuser_id_rate                 4770 non-null float64\nshop_id_rate                 18315 non-null float64\nminofday_rate                18371 non-null float64\ndtypes: float64(13), int64(20), object(3)\nmemory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "test_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKIAAANeCAYAAAAhk6ZmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBsnhWbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab63630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "train_all.hist(bins=50, figsize=(20,15)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_trade                     1.000000\nuser_id_rate                 0.695095\nitem_id_rate                 0.157635\nshop_id_rate                 0.129387\nitem_brand_id_rate           0.106233\nminofday_rate                0.057039\nitem_city_id_rate            0.050501\nitem_sales_level             0.037358\nitem_collected_level         0.013972\nuser_gender_id               0.008843\nitem_pv_level                0.006077\nuser_age_level               0.002628\nuser_star_level              0.002239\nuser_occupation_id           0.002026\ndayofweek                    0.000912\nuser_id                      0.000255\nitem_id                     -0.000168\nshop_star_level             -0.000735\nshop_review_num_level       -0.000747\ninstance_id                 -0.002158\nshop_id                     -0.003238\ncontext_id                  -0.003459\nitem_brand_id               -0.005311\ncontext_timestamp           -0.009274\ncontext_page_id             -0.012313\nitem_city_id                -0.014622\nshop_score_service          -0.015578\nshop_score_delivery         -0.016239\nhourofday                   -0.016492\nminofday                    -0.016631\nshop_score_description      -0.016734\nshop_review_positive_rate   -0.017355\nitem_price_level            -0.055895\nName: is_trade, dtype: float64"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = train_all.corr()\n",
    "corr_matrix[\"is_trade\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_attrib = ['is_trade','instance_id','dayofweek']\n",
    "#delete_attribs = ['instance_id','item_id','item_brand_id','item_city_id','user_id','context_id','shop_id','minofday']\n",
    "text_attribs = ['item_category_list','item_property_list','predict_category_property']\n",
    "# user_id_rate 空缺太多，不使用\n",
    "org_attribs = ['item_id_rate','item_brand_id_rate','item_city_id_rate','shop_review_positive_rate'\n",
    "               ,'shop_score_service','shop_score_delivery','shop_score_description','shop_id_rate','minofday_rate']\n",
    "cat_attribs = ['item_price_level','item_sales_level','item_collected_level','item_pv_level',\n",
    "               'user_gender_id','user_age_level','user_occupation_id','user_star_level','context_page_id'\n",
    "               ,'shop_review_num_level','shop_star_level','dayofweek','hourofday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_all = train_all.append(test_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使得 cat_attribs 全为非负数 \n",
    "for feature in cat_attribs:\n",
    "    max_ = data_all[feature].max()\n",
    "    data_all[feature] = (data_all[feature] - max_) * (-1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(496509, 212)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.pipeline import FeatureUnion \n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values\n",
    "\n",
    " \n",
    "    \n",
    "labels_pipeline = Pipeline([\n",
    "             ('selector', DataFrameSelector(label_attrib)),\n",
    "])   \n",
    "\n",
    "org_pipeline = Pipeline([\n",
    "             ('selector', DataFrameSelector(org_attribs)),\n",
    "             ('imputer', Imputer(strategy=\"mean\")),\n",
    "])   \n",
    "\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "             ('selector', DataFrameSelector(cat_attribs)),\n",
    "             ('imputer', Imputer(strategy=\"most_frequent\")),\n",
    "             ('label_binarizer', OneHotEncoder()),\n",
    "])\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "             (\"labels_pipeline\", labels_pipeline),\n",
    "             (\"org_pipeline\", org_pipeline),\n",
    "             (\"cat_pipeline\", cat_pipeline),\n",
    "])\n",
    "\n",
    "data_prepared = full_pipeline.fit_transform(data_all)\n",
    "data_prepared.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "data_prepared_pd = pd.DataFrame(data_prepared.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_set_s = data_prepared_pd[data_prepared_pd[0] != -1]\n",
    "test_set_s = data_prepared_pd[data_prepared_pd[0] == -1]\n",
    "v_set_s = tmp_set_s[tmp_set_s[2] == 5]\n",
    "train_set_s = tmp_set_s[tmp_set_s[2] != 5]\n",
    "\n",
    "# 正式训练时\n",
    "train_set_s = tmp_set_s\n",
    "\n",
    "y_train = train_set_s[[0]]#.reshape(-1,2)\n",
    "y_v = v_set_s[[0]]\n",
    "y_test = test_set_s[[0]]#.reshape(-1,2)\n",
    "\n",
    "X_train = train_set_s.drop([0,1,2],axis=1)\n",
    "X_v = v_set_s.drop([0,1,2],axis=1)\n",
    "X_test = test_set_s.drop([0,1,2],axis=1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_arr = X_test.values\n",
    "X_train_arr = X_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71236"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "478138"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "\n",
    "def neuron_layer(X, n_neurons, name, activation=None,keep_prob=1.0):\n",
    "    with tf.name_scope(name):\n",
    "        n_inputs = int(X.get_shape()[1])\n",
    "        stddev = 2 / np.sqrt(n_inputs)\n",
    "        init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)\n",
    "        W = tf.Variable(init, name=\"kernel\")\n",
    "        b = tf.Variable(tf.zeros([n_neurons]), name=\"bias\")\n",
    "        Z = tf.matmul(X, W) + b\n",
    "        if activation is not None:\n",
    "            return tf.nn.dropout(activation(Z),keep_prob)\n",
    "        else:\n",
    "            return tf.nn.dropout(Z,keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "num_iters = 100\n",
    "\n",
    "all_size = len(X_train_arr) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn\n",
    "reset_graph()\n",
    "\n",
    "n_features = X_train.shape[1]\n",
    "hidden1_n = 128\n",
    "hidden2_n = 64\n",
    "hidden3_n = 32\n",
    "hidden4_n = 16\n",
    "hidden5_n = 16\n",
    "hidden6_n = 8\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"user_X\")\n",
    "Y = tf.placeholder(tf.float32, shape=(None,1), name=\"y\")\n",
    "\n",
    "keep_prob = tf.placeholder(tf.float32) \n",
    "\n",
    "\n",
    "hidden1 = neuron_layer(X,hidden1_n, name=\"hidden1\",activation=tf.nn.leaky_relu,keep_prob=keep_prob)  \n",
    "hidden2 = neuron_layer(hidden1,hidden2_n, name=\"hidden2\",activation=tf.nn.leaky_relu,keep_prob=keep_prob)\n",
    "hidden3 = neuron_layer(hidden2,hidden3_n, name=\"hidden3\",activation=tf.nn.leaky_relu,keep_prob=keep_prob)\n",
    "hidden4 = neuron_layer(hidden3,hidden4_n, name=\"hidden4\",activation=tf.nn.leaky_relu,keep_prob=keep_prob)\n",
    "hidden5 = neuron_layer(hidden4,hidden5_n, name=\"hidden5\",activation=tf.nn.leaky_relu,keep_prob=keep_prob)\n",
    "hidden6 = neuron_layer(hidden5,hidden6_n, name=\"hidden6\",activation=tf.nn.leaky_relu,keep_prob=keep_prob)\n",
    "\n",
    "output = neuron_layer(hidden6,1, name=\"output\")\n",
    "#cost  = tf.reduce_mean(tf.square(output - Y) * 100)\n",
    "#output = neuron_layer(hidden3,1, name=\"output\")\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits= output, labels=Y))\n",
    "#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=output))  \n",
    "    \n",
    "train_op = tf.train.AdamOptimizer(0.001).minimize(cost)\n",
    "\n",
    "predict_op = tf.nn.sigmoid(output)\n",
    "\n",
    "\n",
    "\n",
    "#correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(Y, 1))  \n",
    "#accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) \n",
    "#matrix2 = tf.constant([[0.], [1.]])\n",
    "#pred = tf.argmax(output, 1)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(71236, 209)"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  0 train------------------------------ cost: 0.405794 ,auc: 0.512997662178\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  0 V ------------------------------ cost: 0.542077 ,auc: 0.507587897204\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  1 train------------------------------ cost: 0.0935437 ,auc: 0.569335693417\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  1 V ------------------------------ cost: 0.0942207 ,auc: 0.561458570782\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  2 train------------------------------ cost: 0.0935569 ,auc: 0.583351483141\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  2 V ------------------------------ cost: 0.0942831 ,auc: 0.581376132789\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  3 train------------------------------ cost: 0.093672 ,auc: 0.580847782713\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  3 V ------------------------------ cost: 0.0942621 ,auc: 0.577684733036\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  4 train------------------------------ cost: 0.0935232 ,auc: 0.620434581919\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  4 V ------------------------------ cost: 0.094231 ,auc: 0.621197484774\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  5 train------------------------------ cost: 0.093413 ,auc: 0.650376110322\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  5 V ------------------------------ cost: 0.0940647 ,auc: 0.644175851185\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  6 train------------------------------ cost: 0.092351 ,auc: 0.678830590031\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  6 V ------------------------------ cost: 0.0930052 ,auc: 0.679297390132\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  7 train------------------------------ cost: 0.0904623 ,auc: 0.699585912698\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  7 V ------------------------------ cost: 0.0910836 ,auc: 0.702000484818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  8 train------------------------------ cost: 0.0893406 ,auc: 0.714328510536\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  8 V ------------------------------ cost: 0.0898538 ,auc: 0.716340311795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  9 train------------------------------ cost: 0.0888823 ,auc: 0.7270960437\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  9 V ------------------------------ cost: 0.0893296 ,auc: 0.730508294455\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  10 train------------------------------ cost: 0.0877731 ,auc: 0.735342680257\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  10 V ------------------------------ cost: 0.0882993 ,auc: 0.739271267601\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  11 train------------------------------ cost: 0.0873531 ,auc: 0.742823630458\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  11 V ------------------------------ cost: 0.0878128 ,auc: 0.747253335047\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  12 train------------------------------ cost: 0.0861889 ,auc: 0.754976498634\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  12 V ------------------------------ cost: 0.0862866 ,auc: 0.762625430996\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  13 train------------------------------ cost: 0.0857556 ,auc: 0.761385094758\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  13 V ------------------------------ cost: 0.0858622 ,auc: 0.770252422822\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  14 train------------------------------ cost: 0.0851112 ,auc: 0.769686834284\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  14 V ------------------------------ cost: 0.0850804 ,auc: 0.780285614994\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  15 train------------------------------ cost: 0.0856464 ,auc: 0.773009279803\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  15 V ------------------------------ cost: 0.0856335 ,auc: 0.783827152911\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  16 train------------------------------ cost: 0.085 ,auc: 0.776558720758\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  16 V ------------------------------ cost: 0.0847245 ,auc: 0.788038771269\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  17 train------------------------------ cost: 0.0842082 ,auc: 0.779926754854\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  17 V ------------------------------ cost: 0.0839222 ,auc: 0.792022718466\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  18 train------------------------------ cost: 0.0839576 ,auc: 0.782561197482\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  18 V ------------------------------ cost: 0.0836543 ,auc: 0.795640166117\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  19 train------------------------------ cost: 0.0844636 ,auc: 0.783783887775\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  19 V ------------------------------ cost: 0.0841426 ,auc: 0.798850462993\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  20 train------------------------------ cost: 0.0837273 ,auc: 0.786573610965\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  20 V ------------------------------ cost: 0.0834873 ,auc: 0.79904226356\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  21 train------------------------------ cost: 0.0834108 ,auc: 0.789297021339\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  21 V ------------------------------ cost: 0.0832144 ,auc: 0.802519415922\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  22 train------------------------------ cost: 0.0837146 ,auc: 0.789849576819\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  22 V ------------------------------ cost: 0.0833038 ,auc: 0.802814928837\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  23 train------------------------------ cost: 0.083325 ,auc: 0.789818764805\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  23 V ------------------------------ cost: 0.0829891 ,auc: 0.802595911364\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  24 train------------------------------ cost: 0.0828489 ,auc: 0.792397248467\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  24 V ------------------------------ cost: 0.082438 ,auc: 0.804690220108\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  25 train------------------------------ cost: 0.0828007 ,auc: 0.793406526214\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  25 V ------------------------------ cost: 0.0825314 ,auc: 0.807159133314\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  26 train------------------------------ cost: 0.0826296 ,auc: 0.795531677217\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  26 V ------------------------------ cost: 0.0824896 ,auc: 0.808114548033\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  27 train------------------------------ cost: 0.0825724 ,auc: 0.797168846976\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  27 V ------------------------------ cost: 0.0823782 ,auc: 0.809793458613\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  28 train------------------------------ cost: 0.0827301 ,auc: 0.797269357988\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  28 V ------------------------------ cost: 0.0824389 ,auc: 0.80969656896\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  29 train------------------------------ cost: 0.0824675 ,auc: 0.80103201914\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  29 V ------------------------------ cost: 0.0820253 ,auc: 0.814578674061\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  30 train------------------------------ cost: 0.082285 ,auc: 0.799925311145\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  30 V ------------------------------ cost: 0.0820787 ,auc: 0.81341515396\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  31 train------------------------------ cost: 0.0822153 ,auc: 0.799411711766\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  31 V ------------------------------ cost: 0.0820259 ,auc: 0.812607495698\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  32 train------------------------------ cost: 0.0821296 ,auc: 0.802436679042\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  32 V ------------------------------ cost: 0.0820164 ,auc: 0.814931422683\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  33 train------------------------------ cost: 0.0822856 ,auc: 0.802252117811\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  33 V ------------------------------ cost: 0.0821501 ,auc: 0.815143844357\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  34 train------------------------------ cost: 0.0822682 ,auc: 0.802530429387\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  34 V ------------------------------ cost: 0.0819847 ,auc: 0.816514372299\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  35 train------------------------------ cost: 0.082346 ,auc: 0.804907028765\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  35 V ------------------------------ cost: 0.0819548 ,auc: 0.820231627586\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  36 train------------------------------ cost: 0.0822695 ,auc: 0.804094129269\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  36 V ------------------------------ cost: 0.0819858 ,auc: 0.81743588461\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  37 train------------------------------ cost: 0.0821166 ,auc: 0.806753588884\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  37 V ------------------------------ cost: 0.0819619 ,auc: 0.819485990944\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  38 train------------------------------ cost: 0.0819579 ,auc: 0.803902824383\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  38 V ------------------------------ cost: 0.0818475 ,auc: 0.81705028363\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  39 train------------------------------ cost: 0.0818199 ,auc: 0.806048020025\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  39 V ------------------------------ cost: 0.081709 ,auc: 0.818873478639\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  40 train------------------------------ cost: 0.0819771 ,auc: 0.807452260849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  40 V ------------------------------ cost: 0.0819459 ,auc: 0.820064706374\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  41 train------------------------------ cost: 0.081747 ,auc: 0.808002066149\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  41 V ------------------------------ cost: 0.0815453 ,auc: 0.821772079148\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  42 train------------------------------ cost: 0.0817613 ,auc: 0.808931147261\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  42 V ------------------------------ cost: 0.0816001 ,auc: 0.821773234732\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  43 train------------------------------ cost: 0.0814328 ,auc: 0.808671859153\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  43 V ------------------------------ cost: 0.0815429 ,auc: 0.820129350484\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  44 train------------------------------ cost: 0.0814456 ,auc: 0.8102122453\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  44 V ------------------------------ cost: 0.0814382 ,auc: 0.823200381016\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  45 train------------------------------ cost: 0.0815231 ,auc: 0.811015714526\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  45 V ------------------------------ cost: 0.081433 ,auc: 0.826002244893\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  46 train------------------------------ cost: 0.0818345 ,auc: 0.810649671881\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  46 V ------------------------------ cost: 0.0815662 ,auc: 0.825772162308\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  47 train------------------------------ cost: 0.0820335 ,auc: 0.811303834689\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  47 V ------------------------------ cost: 0.0820106 ,auc: 0.82503939539\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  48 train------------------------------ cost: 0.0818179 ,auc: 0.812801098761\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  48 V ------------------------------ cost: 0.0818674 ,auc: 0.826697895929\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  49 train------------------------------ cost: 0.0820138 ,auc: 0.813236407974\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  49 V ------------------------------ cost: 0.0822646 ,auc: 0.827775227402\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  50 train------------------------------ cost: 0.0816662 ,auc: 0.812478922182\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  50 V ------------------------------ cost: 0.0819984 ,auc: 0.827805694718\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  51 train------------------------------ cost: 0.081394 ,auc: 0.813680197176\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  51 V ------------------------------ cost: 0.0814249 ,auc: 0.827168793786\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  52 train------------------------------ cost: 0.0819832 ,auc: 0.814867028318\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  52 V ------------------------------ cost: 0.0819452 ,auc: 0.82813920999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  53 train------------------------------ cost: 0.0810676 ,auc: 0.815553541808\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  53 V ------------------------------ cost: 0.080992 ,auc: 0.829220440899\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  54 train------------------------------ cost: 0.0814374 ,auc: 0.816152685179\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  54 V ------------------------------ cost: 0.0812374 ,auc: 0.83091869564\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  55 train------------------------------ cost: 0.0810712 ,auc: 0.815276891423\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  55 V ------------------------------ cost: 0.0812493 ,auc: 0.827761107115\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  56 train------------------------------ cost: 0.0815736 ,auc: 0.81697534222\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  56 V ------------------------------ cost: 0.0815774 ,auc: 0.830195838251\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  57 train------------------------------ cost: 0.0811909 ,auc: 0.815321222645\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  57 V ------------------------------ cost: 0.081116 ,auc: 0.830430289894\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  58 train------------------------------ cost: 0.0812774 ,auc: 0.817496416011\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  58 V ------------------------------ cost: 0.0812839 ,auc: 0.831458036786\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  59 train------------------------------ cost: 0.0809878 ,auc: 0.816275690909\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  59 V ------------------------------ cost: 0.0811405 ,auc: 0.829345776916\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  60 train------------------------------ cost: 0.0810492 ,auc: 0.816033447846\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  60 V ------------------------------ cost: 0.0811687 ,auc: 0.831026845642\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  61 train------------------------------ cost: 0.0808843 ,auc: 0.817348347806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  61 V ------------------------------ cost: 0.080861 ,auc: 0.833735339385\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  62 train------------------------------ cost: 0.0809276 ,auc: 0.817973064887\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  62 V ------------------------------ cost: 0.0810806 ,auc: 0.832135921377\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  63 train------------------------------ cost: 0.0811822 ,auc: 0.819646496925\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  63 V ------------------------------ cost: 0.0813708 ,auc: 0.834711538786\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  64 train------------------------------ cost: 0.0809606 ,auc: 0.818210910521\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  64 V ------------------------------ cost: 0.0809646 ,auc: 0.832248693715\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  65 train------------------------------ cost: 0.0809548 ,auc: 0.817630674595\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  65 V ------------------------------ cost: 0.0809869 ,auc: 0.83236473757\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  66 train------------------------------ cost: 0.0811222 ,auc: 0.819346108172\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  66 V ------------------------------ cost: 0.0814676 ,auc: 0.835943454688\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  67 train------------------------------ cost: 0.0806712 ,auc: 0.820415811552\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  67 V ------------------------------ cost: 0.0809146 ,auc: 0.834964147346\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  68 train------------------------------ cost: 0.0808464 ,auc: 0.820448093944\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  68 V ------------------------------ cost: 0.0811261 ,auc: 0.835524626709\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  69 train------------------------------ cost: 0.0811355 ,auc: 0.821290562515\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  69 V ------------------------------ cost: 0.0814355 ,auc: 0.836111805869\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  70 train------------------------------ cost: 0.0809383 ,auc: 0.82223331076\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  70 V ------------------------------ cost: 0.0812036 ,auc: 0.836244059559\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  71 train------------------------------ cost: 0.0805537 ,auc: 0.822540918839\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  71 V ------------------------------ cost: 0.0806876 ,auc: 0.838010995039\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  72 train------------------------------ cost: 0.0806821 ,auc: 0.820757047399\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  72 V ------------------------------ cost: 0.0804985 ,auc: 0.837798145958\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  73 train------------------------------ cost: 0.0805897 ,auc: 0.822080015475\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  73 V ------------------------------ cost: 0.0807754 ,auc: 0.836952411466\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  74 train------------------------------ cost: 0.0812227 ,auc: 0.820548360149\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  74 V ------------------------------ cost: 0.0811395 ,auc: 0.835448558675\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  75 train------------------------------ cost: 0.0808855 ,auc: 0.823408220201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  75 V ------------------------------ cost: 0.0809964 ,auc: 0.837442252458\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  76 train------------------------------ cost: 0.0806064 ,auc: 0.821995097443\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  76 V ------------------------------ cost: 0.0806576 ,auc: 0.836970246508\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  77 train------------------------------ cost: 0.0803249 ,auc: 0.820656858936\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  77 V ------------------------------ cost: 0.0804907 ,auc: 0.834892511689\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  78 train------------------------------ cost: 0.0807333 ,auc: 0.822506334764\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  78 V ------------------------------ cost: 0.0808973 ,auc: 0.836963112491\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  79 train------------------------------ cost: 0.0806095 ,auc: 0.822319944923\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  79 V ------------------------------ cost: 0.0808762 ,auc: 0.83672242386\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  80 train------------------------------ cost: 0.0809705 ,auc: 0.825391126972\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  80 V ------------------------------ cost: 0.081263 ,auc: 0.839402603156\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  81 train------------------------------ cost: 0.0803417 ,auc: 0.825387246449\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  81 V ------------------------------ cost: 0.0805866 ,auc: 0.839824291073\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  82 train------------------------------ cost: 0.0810048 ,auc: 0.826172450022\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  82 V ------------------------------ cost: 0.0813076 ,auc: 0.841854741926\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  83 train------------------------------ cost: 0.0808697 ,auc: 0.82680764312\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  83 V ------------------------------ cost: 0.0811704 ,auc: 0.841189917017\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  84 train------------------------------ cost: 0.0802603 ,auc: 0.827174564092\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  84 V ------------------------------ cost: 0.0804618 ,auc: 0.841436119737\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  85 train------------------------------ cost: 0.0801921 ,auc: 0.82396192859\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  85 V ------------------------------ cost: 0.0805224 ,auc: 0.837739501387\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  86 train------------------------------ cost: 0.0803586 ,auc: 0.8255826144\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  86 V ------------------------------ cost: 0.0807256 ,auc: 0.841293413023\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  87 train------------------------------ cost: 0.0808174 ,auc: 0.825877686397\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  87 V ------------------------------ cost: 0.0813374 ,auc: 0.839802498552\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  88 train------------------------------ cost: 0.0805129 ,auc: 0.826633133036\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  88 V ------------------------------ cost: 0.0809014 ,auc: 0.841461637565\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  89 train------------------------------ cost: 0.0802805 ,auc: 0.825673052051\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  89 V ------------------------------ cost: 0.0808724 ,auc: 0.841026958562\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  90 train------------------------------ cost: 0.080422 ,auc: 0.827388346802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  90 V ------------------------------ cost: 0.0811688 ,auc: 0.84330247238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  91 train------------------------------ cost: 0.0808619 ,auc: 0.82684401516\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  91 V ------------------------------ cost: 0.0814487 ,auc: 0.842828244964\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  92 train------------------------------ cost: 0.0805399 ,auc: 0.827488019893\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  92 V ------------------------------ cost: 0.0809836 ,auc: 0.842843251727\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  93 train------------------------------ cost: 0.080811 ,auc: 0.826410405891\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  93 V ------------------------------ cost: 0.0811595 ,auc: 0.841233665635\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  94 train------------------------------ cost: 0.0804996 ,auc: 0.825168466227\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  94 V ------------------------------ cost: 0.0811359 ,auc: 0.840352820385\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  95 train------------------------------ cost: 0.0804191 ,auc: 0.827534141209\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  95 V ------------------------------ cost: 0.0811176 ,auc: 0.84230537221\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  96 train------------------------------ cost: 0.0808857 ,auc: 0.827007165701\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  96 V ------------------------------ cost: 0.0815101 ,auc: 0.842712728774\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  97 train------------------------------ cost: 0.0802357 ,auc: 0.828240160338\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  97 V ------------------------------ cost: 0.0810646 ,auc: 0.842770022525\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  98 train------------------------------ cost: 0.0806361 ,auc: 0.829175853721\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  98 V ------------------------------ cost: 0.0815185 ,auc: 0.844571694122\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  99 train------------------------------ cost: 0.0802609 ,auc: 0.82854869814\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iters :  99 V ------------------------------ cost: 0.0810769 ,auc: 0.84358667218\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    \n",
    "    for i in range(num_iters):\n",
    "        predicts,cost_ = sess.run([predict_op,cost], feed_dict={X:X_train, Y: y_train,keep_prob:1.0})\n",
    "        print ('iters : ',i, 'train------------------------------ cost:', \n",
    "               cost_,',auc:',roc_auc_score(y_train ,predicts))\n",
    "        predicts,cost_ = sess.run([predict_op,cost], feed_dict={X:X_v, Y: y_v,keep_prob:1.0})\n",
    "        print ('iters : ',i, 'V ------------------------------ cost:', \n",
    "               cost_,',auc:',roc_auc_score(y_v ,predicts))\n",
    "        step = 0\n",
    "        for start, end in zip(range(0, all_size, batch_size), range(batch_size, all_size, batch_size)):\n",
    "            step = step + 1\n",
    "            #np.random.seed(i * batch_size + step) # 每次调用 都有不同的 随机种子\n",
    "            #indices = np.random.randint(all_size,size=batch_size) # 去 0 ~ m-1 之间取 batch_size 整数\n",
    "            sess.run(train_op, feed_dict={X: X_train_arr[start: end],Y: y_train[start: end],keep_prob:0.5})\n",
    "          \n",
    "            #if (step % 500) == 0:\n",
    "            #    predicts,cost_ = sess.run([predict_op,cost], \n",
    "            #                              feed_dict={X: X_train_arr[start: end],Y: y_train[start: end],keep_prob:1.0})\n",
    "            #    print ('iters : ',i,'step:',step, 'cost_:', cost_,'auc:',roc_auc_score(y_train[start: end] ,predicts))\n",
    "    \n",
    "    predicts = sess.run(predict_op, feed_dict={X:X_test,keep_prob:1.0})\n",
    "    pd.DataFrame(predicts).to_csv(\"./tianchi_ad/result.csv\",index=False,float_format = '%.4f')\n",
    "            \n",
    "            \n",
    "     \n",
    "\n",
    "     \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(406902, 209)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7665, 1)"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[y_train[0] == 1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18371, 1)"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_predicts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_predicts = pd.DataFrame(predicts) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10615</th>\n",
       "      <td>0.960380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11529</th>\n",
       "      <td>0.877262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13542</th>\n",
       "      <td>0.882162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10615</th>\n",
       "      <td>0.960380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11529</th>\n",
       "      <td>0.877262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13542</th>\n",
       "      <td>0.882162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_predicts[pd_predicts[0] > 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18368, 1)"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_predicts[pd_predicts[0] < 0.5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.194152e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.878600e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.484671e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.988288e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.607968e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.335810e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.765747e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.516578e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.798444e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.810450e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.782454e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.448268e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.273599e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.683000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.027243e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.149804e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.352321e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.204536e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.166945e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.086390e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.137039e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.605393e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.716239e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.804618e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.008765e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.991394e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.357175e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.566495e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.655786e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.853371e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18341</th>\n",
       "      <td>1.343727e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18342</th>\n",
       "      <td>1.962505e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18343</th>\n",
       "      <td>1.197606e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18344</th>\n",
       "      <td>1.727248e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18345</th>\n",
       "      <td>9.998675e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18346</th>\n",
       "      <td>1.879502e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18347</th>\n",
       "      <td>1.133969e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18348</th>\n",
       "      <td>1.976151e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18349</th>\n",
       "      <td>1.998865e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18350</th>\n",
       "      <td>1.031971e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18351</th>\n",
       "      <td>8.994335e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18352</th>\n",
       "      <td>5.219539e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18353</th>\n",
       "      <td>7.389627e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18354</th>\n",
       "      <td>2.633011e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18355</th>\n",
       "      <td>1.463207e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18356</th>\n",
       "      <td>2.556759e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18357</th>\n",
       "      <td>2.327142e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18358</th>\n",
       "      <td>1.982675e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18359</th>\n",
       "      <td>4.625987e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18360</th>\n",
       "      <td>3.830079e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18361</th>\n",
       "      <td>2.732162e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18362</th>\n",
       "      <td>4.679460e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18363</th>\n",
       "      <td>3.302186e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18364</th>\n",
       "      <td>8.281128e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18365</th>\n",
       "      <td>7.998699e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18366</th>\n",
       "      <td>1.922929e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18367</th>\n",
       "      <td>2.201173e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18368</th>\n",
       "      <td>1.427752e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18369</th>\n",
       "      <td>3.795492e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18370</th>\n",
       "      <td>1.526578e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18371 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.194152e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.878600e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.484671e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.988288e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.607968e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.335810e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.765747e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.516578e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.798444e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.810450e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.782454e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.448268e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.273599e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.683000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.027243e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.149804e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.352321e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.204536e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.166945e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.086390e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.137039e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.605393e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.716239e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.804618e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.008765e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.991394e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.357175e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.566495e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.655786e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.853371e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18341</th>\n",
       "      <td>1.343727e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18342</th>\n",
       "      <td>1.962505e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18343</th>\n",
       "      <td>1.197606e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18344</th>\n",
       "      <td>1.727248e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18345</th>\n",
       "      <td>9.998675e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18346</th>\n",
       "      <td>1.879502e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18347</th>\n",
       "      <td>1.133969e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18348</th>\n",
       "      <td>1.976151e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18349</th>\n",
       "      <td>1.998865e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18350</th>\n",
       "      <td>1.031971e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18351</th>\n",
       "      <td>8.994335e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18352</th>\n",
       "      <td>5.219539e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18353</th>\n",
       "      <td>7.389627e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18354</th>\n",
       "      <td>2.633011e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18355</th>\n",
       "      <td>1.463207e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18356</th>\n",
       "      <td>2.556759e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18357</th>\n",
       "      <td>2.327142e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18358</th>\n",
       "      <td>1.982675e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18359</th>\n",
       "      <td>4.625987e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18360</th>\n",
       "      <td>3.830079e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18361</th>\n",
       "      <td>2.732162e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18362</th>\n",
       "      <td>4.679460e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18363</th>\n",
       "      <td>3.302186e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18364</th>\n",
       "      <td>8.281128e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18365</th>\n",
       "      <td>7.998699e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18366</th>\n",
       "      <td>1.922929e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18367</th>\n",
       "      <td>2.201173e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18368</th>\n",
       "      <td>1.427752e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18369</th>\n",
       "      <td>3.795492e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18370</th>\n",
       "      <td>1.526578e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18371 rows × 1 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_predicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "re = train_set['instance_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_p = pd_predicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_p[2] = re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_p[[2,0]].to_csv(\"./tianchi_ad/submit.txt\",sep=' ',index=False,float_format='%.4f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
